Construction Unit Rate Factor Modelling Using Neural Networks
Factors affecting construction unit cost vary
depending on a country’s political, economic, social and
technological inclinations. Factors affecting construction costs have
been studied from various perspectives. Analysis of cost factors
requires an appreciation of a country’s practices. Identified cost
factors provide an indication of a country’s construction economic
strata. The purpose of this paper is to identify the essential factors
that affect unit cost estimation and their breakdown using artificial
neural networks. Twenty five (25) identified cost factors in road
construction were subjected to a questionnaire survey and employing
SPSS factor analysis the factors were reduced to eight. The 8 factors
were analysed using neural network (NN) to determine the
proportionate breakdown of the cost factors in a given construction
unit rate. NN predicted that political environment accounted 44% of
the unit rate followed by contractor capacity at 22% and financial
delays, project feasibility and overhead & profit each at 11%. Project
location, material availability and corruption perception index had
minimal impact on the unit cost from the training data provided.
Quantified cost factors can be incorporated in unit cost estimation
models (UCEM) to produce more accurate estimates. This can create
improvements in the cost estimation of infrastructure projects and
establish a benchmark standard to assist the process of alignment of
work practises and training of new staff, permitting the on-going
development of best practises in cost estimation to become more
effective.
[1] G. Raballand and A. Whitworth “The Crisis in the Zambian Road
Sector”, Working Paper No. 5, Zambia Institute for Policy Analysis and
Research (ZIPAR), 2012, Lusaka.
[2] C. Hendrickson, Project Management for Construction, Fundamental
Concepts for Owners, Engineers, Architects and Builders, 2008,
(available online: http://pmbook.ce.cmu.edu/ (accessed 05/11/20139))
[3] Zambia Public Procurement Authority, Consulting services to undertake
an assessment of the prevailing market rates in the construction industry,
Request for proposals, ZPPA, Zambia, 2014.
[4] J. De la Garza and K. Rouhana, Neural Network versus parameter-based
application. Cost Engineering, 37(2) 1995, pp.14-18.
[5] A. K. Mason and A. E. Smith, Cost Estimation Predictive Modeling:
Regression versus Neural Network. Engineering Economist 42, (2)
1997, pp. 137-161. [6] AACE International, Cost Engineering Terminology, Recommended
Practice No. 10S-90, TCM Framework: General Reference, Association
for the Advancement of Cost Engineering (AACE),2013
[7] Langdon, D., Spon's Civil Engineering and Highway Works Price Book
2009, Spon Press, 2009
[8] J. Mashilipa, An investigation on the effects and their implications of
using British outputs in Estimating in the Zambian Construction
Industry, BSc (Building) Unpublished thesis, Copperbelt University,
Zambia, 2004.
[9] Ibid
[10] E. J Blocher, E. David, G. C. Stout and K. H. Chen, Cost management;
A strategic emphasis 4th ed., McGraw Hill, 2008.
[11] A. Enshassi, S. Mohamed, and I. Madi, Contractors’ Perspectives
towards Factors Affecting Cost Estimation in Palestine. Jordan Journal
of Civil Engineering, 1 (2) 2007, pp. 186-193.
[12] A. Trombka and S. Downey, A Study of County Road Project Cost and
Schedule Estimates, Office of Legislative Oversight, Montgomery
County, Maryland Report Number 2008-04, 2008, (available online
https://www.montgomerycountymd.gov/olo/resources/files/2008-4.pdf
(accessed 05/07/2014))
[13] A.H. Memon, I.A. Rahman, M.R. Abdullah, and A.A.A. Azis, Factors
Affecting Construction Cost Performance in Project Management
Projects: Case of Mara Large Projects. Proceedings of Post Graduate
seminar on Engineering, Technology and Social Science. 29-30
November 2010, Universiti Tun Hussein Onn Malaysia, 2010
[14] A.U. Elinwa and S.A. Buba, Construction cost factors in Nigeria,
Journal of Construction Engineering and Management. 119 (4), 1993,
pp. 698-713.
[15] J. Bode, Neural Networks for Cost Estimation, Cost Engineering Journal
40 (1) 1998, pp.25-30
[16] I. Peško, M. Trivunić, G. Cirović and V. Mučenski, A preliminary
estimate of time and cost in urban road construction using neural
networks Technical Gazette 20 (3), 2013, pp. 563-570 (accessed
16/01/2014)
[17] A. J. Hasan, Parametric Cost Estimation of Road Projects Using
Artificial Neural Networks, M.Sc. Unpublished thesis, The Islamic
University, Gaza, 2013
[18] S. Muqeem, A. Idrus, F. M. Khamidi, J.B. Ahmad, and S.B. Zakaria,
Construction labor production rates modeling using artificial neural
network, Journal of Information Technology in Construction (ITcon),
Vol. 16, 2011, pp. 713-726, (available online
http://www.itcon.org/2011/42 (accessed on 09/04/2014))
[19] A. Hashem, P.A. Alex, and M. Tantash, Preliminary Cost Estimation of
Highway Construction Using Neural Networks, Cost Engineering 41, (3)
1999, pp. 19-24
[20] D. R. Cooper and P.S. Schindler, Business research methods 11th ed.,
McGraw Hill, 2011, pp.390-397
[21] Creative Research Systems, Sample Size Formula, (available online:
http://www.surveysystem.com/sample-size-formula.htm (accessed on
09/04/2014))
[22] J. C. F. de Winter, D. Dodou, and P. A. Wieringa, Exploratory Factor
Analysis with Small Sample Sizes, Multivariate Behavioral Research,
44, 2009, pp147–181
[1] G. Raballand and A. Whitworth “The Crisis in the Zambian Road
Sector”, Working Paper No. 5, Zambia Institute for Policy Analysis and
Research (ZIPAR), 2012, Lusaka.
[2] C. Hendrickson, Project Management for Construction, Fundamental
Concepts for Owners, Engineers, Architects and Builders, 2008,
(available online: http://pmbook.ce.cmu.edu/ (accessed 05/11/20139))
[3] Zambia Public Procurement Authority, Consulting services to undertake
an assessment of the prevailing market rates in the construction industry,
Request for proposals, ZPPA, Zambia, 2014.
[4] J. De la Garza and K. Rouhana, Neural Network versus parameter-based
application. Cost Engineering, 37(2) 1995, pp.14-18.
[5] A. K. Mason and A. E. Smith, Cost Estimation Predictive Modeling:
Regression versus Neural Network. Engineering Economist 42, (2)
1997, pp. 137-161. [6] AACE International, Cost Engineering Terminology, Recommended
Practice No. 10S-90, TCM Framework: General Reference, Association
for the Advancement of Cost Engineering (AACE),2013
[7] Langdon, D., Spon's Civil Engineering and Highway Works Price Book
2009, Spon Press, 2009
[8] J. Mashilipa, An investigation on the effects and their implications of
using British outputs in Estimating in the Zambian Construction
Industry, BSc (Building) Unpublished thesis, Copperbelt University,
Zambia, 2004.
[9] Ibid
[10] E. J Blocher, E. David, G. C. Stout and K. H. Chen, Cost management;
A strategic emphasis 4th ed., McGraw Hill, 2008.
[11] A. Enshassi, S. Mohamed, and I. Madi, Contractors’ Perspectives
towards Factors Affecting Cost Estimation in Palestine. Jordan Journal
of Civil Engineering, 1 (2) 2007, pp. 186-193.
[12] A. Trombka and S. Downey, A Study of County Road Project Cost and
Schedule Estimates, Office of Legislative Oversight, Montgomery
County, Maryland Report Number 2008-04, 2008, (available online
https://www.montgomerycountymd.gov/olo/resources/files/2008-4.pdf
(accessed 05/07/2014))
[13] A.H. Memon, I.A. Rahman, M.R. Abdullah, and A.A.A. Azis, Factors
Affecting Construction Cost Performance in Project Management
Projects: Case of Mara Large Projects. Proceedings of Post Graduate
seminar on Engineering, Technology and Social Science. 29-30
November 2010, Universiti Tun Hussein Onn Malaysia, 2010
[14] A.U. Elinwa and S.A. Buba, Construction cost factors in Nigeria,
Journal of Construction Engineering and Management. 119 (4), 1993,
pp. 698-713.
[15] J. Bode, Neural Networks for Cost Estimation, Cost Engineering Journal
40 (1) 1998, pp.25-30
[16] I. Peško, M. Trivunić, G. Cirović and V. Mučenski, A preliminary
estimate of time and cost in urban road construction using neural
networks Technical Gazette 20 (3), 2013, pp. 563-570 (accessed
16/01/2014)
[17] A. J. Hasan, Parametric Cost Estimation of Road Projects Using
Artificial Neural Networks, M.Sc. Unpublished thesis, The Islamic
University, Gaza, 2013
[18] S. Muqeem, A. Idrus, F. M. Khamidi, J.B. Ahmad, and S.B. Zakaria,
Construction labor production rates modeling using artificial neural
network, Journal of Information Technology in Construction (ITcon),
Vol. 16, 2011, pp. 713-726, (available online
http://www.itcon.org/2011/42 (accessed on 09/04/2014))
[19] A. Hashem, P.A. Alex, and M. Tantash, Preliminary Cost Estimation of
Highway Construction Using Neural Networks, Cost Engineering 41, (3)
1999, pp. 19-24
[20] D. R. Cooper and P.S. Schindler, Business research methods 11th ed.,
McGraw Hill, 2011, pp.390-397
[21] Creative Research Systems, Sample Size Formula, (available online:
http://www.surveysystem.com/sample-size-formula.htm (accessed on
09/04/2014))
[22] J. C. F. de Winter, D. Dodou, and P. A. Wieringa, Exploratory Factor
Analysis with Small Sample Sizes, Multivariate Behavioral Research,
44, 2009, pp147–181
@article{"International Journal of Architectural, Civil and Construction Sciences:68835", author = "Balimu Mwiya and Mundia Muya and Chabota Kaliba and Peter Mukalula", title = "Construction Unit Rate Factor Modelling Using Neural Networks", abstract = "Factors affecting construction unit cost vary
depending on a country’s political, economic, social and
technological inclinations. Factors affecting construction costs have
been studied from various perspectives. Analysis of cost factors
requires an appreciation of a country’s practices. Identified cost
factors provide an indication of a country’s construction economic
strata. The purpose of this paper is to identify the essential factors
that affect unit cost estimation and their breakdown using artificial
neural networks. Twenty five (25) identified cost factors in road
construction were subjected to a questionnaire survey and employing
SPSS factor analysis the factors were reduced to eight. The 8 factors
were analysed using neural network (NN) to determine the
proportionate breakdown of the cost factors in a given construction
unit rate. NN predicted that political environment accounted 44% of
the unit rate followed by contractor capacity at 22% and financial
delays, project feasibility and overhead & profit each at 11%. Project
location, material availability and corruption perception index had
minimal impact on the unit cost from the training data provided.
Quantified cost factors can be incorporated in unit cost estimation
models (UCEM) to produce more accurate estimates. This can create
improvements in the cost estimation of infrastructure projects and
establish a benchmark standard to assist the process of alignment of
work practises and training of new staff, permitting the on-going
development of best practises in cost estimation to become more
effective.
", keywords = "Construction cost factors, neural networks,
roadworks, Zambian Construction Industry.", volume = "9", number = "1", pages = "29-6", }
